Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer

MiRNAs as new potential biomarkers and therapeutic targets in brain metastasis

Brain metastases represent a formidable challenge in cancer management, impacting a significant number of patients and contributing significantly to cancer-related mortality. Conventional diagnostic methods frequently fall short, underscoring the imperative for non-invasive alternatives. Non-coding RNAs (ncRNAs), specifically microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), present promising avenues for exploration. These ncRNAs exert influence over the prognosis and treatment resistance of brain metastases, offering valuable insights into underlying mechanisms and potential therapeutic targets. Dysregulated ncRNAs have been identified in brain metastases originating from various primary cancers, unveiling opportunities for intervention and prevention. The analysis of ncRNA expression in bodily fluids, such as serum and cerebrospinal fluid, provides a noninvasive means to differentiate brain metastases from primary tumors. NcRNAs, particularly miRNAs, assume a pivotal role in orchestrating the immune response within the brain microenvironment. MiRNAs exhibit promise in diagnosing brain metastases, effectively distinguishing between normal and cancer cells, and pinpointing the tissue of origin for metastatic brain tumors. The manipulation of miRNAs holds substantial potential in cancer treatment, offering the prospect of reducing toxicity and enhancing efficacy. Given the limited treatment options and the formidable threat of brain metastases in cancer patients, non-coding RNAs, especially miRNAs, emerge as beacons of hope, serving as both diagnostic tools and therapeutic targets. Further clinical studies are imperative to validate the specificity and sensitivity of ncRNAs, potentially reshaping approaches to tackle this challenge and elevate treatment outcomes for affected patients.

MicroRNA-98: the multifaceted regulator in human cancer progression and therapy

MicroRNA-98 (miR-98) stands as an important molecule in the intricate landscape of oncology. As a subset of microRNAs, these small non-coding RNAs have accompanied a new era in cancer research, underpinning their significant roles in tumorigenesis, metastasis, and therapeutic interventions. This review provides a comprehensive insight into the biogenesis, molecular properties, and physiological undertakings of miR-98, highlighting its double-edged role in cancer progression-acting both as a tumor promoter and suppressor. Intriguingly, miR-98 has profound implications for various aspects of cancer progression, modulating key cellular functions, including proliferation, apoptosis, and the cell cycle. Given its expression patterns, the potential of miR-98 as a diagnostic and prognostic biomarker, especially in liquid biopsies and tumor tissues, is explored, emphasizing the hurdles in translating these findings clinically. The review concludes by evaluating therapeutic avenues to modulate miR-98 expression, addressing the challenges in therapy resistance, and assessing the efficacy of miR-98 interventions. In conclusion, while miR-98's involvement in cancer showcases promising diagnostic and therapeutic avenues, future research should pivot towards understanding its role in tumor-stroma interactions, immune modulation, and metabolic regulation, thereby unlocking novel strategies for cancer management.

Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development

BACKGROUND

Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of nodal metastasis; however, its preoperative assessment is challenging.

OBJECTIVE

This paper aimed to externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based cohort (n=18,633) and develop a new MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC, 2014-2017), comprising only routinely and preoperatively available documented clinicopathological variables. A secondary aim was to develop and validate an LVI MLP for imputation of missing LVI status to increase the preoperative feasibility of the original NILS model.

METHODS

Three nonoverlapping cohorts were used for model development and validation. A total of 4 MLPs for nodal status and 1 LVI MLP were developed using 11 to 12 routinely available predictors. Three nodal status models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth nodal status model was developed for 80% (14,906/18,663) of NKBC cases and validated in the remaining 20% (3727/18,663). Three alternatives for imputation of LVI status were compared. The discriminatory capacity was evaluated using the validation area under the receiver operating characteristics curve (AUC) in 3 of the nodal status models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.

RESULTS

External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI 0.694-0.799) with good calibration but did not improve the discriminatory performance of the nodal status models. A new nodal status model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729), with excellent calibration in the holdout internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNBs.

CONCLUSIONS

The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model's discriminatory performance. A new nodal status model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.

Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer

Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers.

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

Nomograms predict survival of patients with lymph node-positive, luminal a breast cancer

Background

To develop nomograms for the prediction of the 1-, 3-, and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) for patients with lymph node positive, luminal A breast cancer.

Methods

Thirty-nine thousand fifty-one patients from The Surveillance, Epidemiology, and End Results (SEER) database were included in our study and were set into a training group (n = 19,526) and a validation group (n = 19,525). Univariate analysis and Cox proportional hazards analysis were used to select variables and set up nomogram models on the basis of the training group. Kaplan-Meier curves and the log-rank test were adopted in the survival analysis and curves plotting. C-index, calibration plots and ROC curves were used to performed internal and external validation on the training group and validation group.

Results

Following independent factors were included in our nomograms: Age, marital status, grade, ethnic group, T stage, positive lymph nodes numbers, Metastasis, surgery, radiotherapy, chemotherapy. In both the training group and testing group, the calibration plots show that the actual and nomogram-predicted survival probabilities are consistent greatly. The C-index values of the nomograms in the training and validation cohorts were 0.782 and 0.806 for OS and 0.783 and 0.804 for BCSS, respectively. The ROC curves show that our nomograms have good discrimination.

Conclusions

The nomograms may assist clinicians predict the 1-, 3-, and 5-year OS and BCSS of patients with lymph node positive, luminal A breast cancer.

The Role of MicroRNA as Clinical Biomarkers for Breast Cancer Surgery and Treatment

Breast cancer is the most common cancer diagnosed in women. In recent times, survival outcomes have improved dramatically in accordance with our enhanced understanding of the molecular processes driving breast cancer proliferation and development. Refined surgical approaches, combined with novel and targeted treatment options, have aided the personalisation of breast cancer patient care. Despite this, some patients will unfortunately succumb to the disease. In recent times, translational research efforts have been focused on identifying novel biomarkers capable of informing patient outcome; microRNAs (miRNAs) are small non-coding molecules, which regulate gene expression at a post-transcriptional level. Aberrant miRNA expression profiles have been observed in cancer proliferation and development. The measurement and correlation of miRNA expression levels with oncological outcomes such as response to current conventional therapies, and disease recurrence are being investigated. Herein, we outline the clinical utility of miRNA expression profiles in informing breast cancer prognosis, predicting response to treatment strategies as well as their potential as therapeutic targets to enhance treatment modalities in the era of precision oncology.

The Guiding Significance of the Number of Positive Sentinel Lymph Nodes in Frozen Section for Intraoperative Axillary Dissection in Early Breast Cancer

Purpose

The results of large randomised trials have changed the treatment strategy of axillary lymph nodes. Axillary lymph node dissection (ALND) can be avoided in some patients with one to two sentinel lymph nodes (SLNs) metastasis based on final paraffin section (FPS) results which called into question the need for intraoperative frozen section (FS). This study aims to assess the guiding value of the number of positive SLN detected via FS for intraoperative ALND.

Patients and Methods

This study retrospectively analyzed data from 3303 patients with breast cancer who underwent SLN biopsy between 2015 and 2019. Combined with the FPS results, FS sensitivity, specificity, and false negative rate (FNR) were calculated and the difference in the number of positive SLNs between FS and FPS was analyzed.

Results

The overall FNR of FS was 23.21%, which was 76.47% in isolated tumor cells, 62.28% in micrometastasis, and 12.09% in macrometastatic disease. The size of SLN metastasis were significantly associated with a higher FNR (p<0.001). The accuracy rate of the number of positive SLNs detected via FS was 92.62%. Human epidermal growth factor receptor 2 (HER2) (p<0.03) and Ki67 (p<0.02) were significant factors affecting the accuracy rate.

Conclusion

FS is a effective method for SLN biopsy, ALND can be avoided in patients with one or two positive SLNs detected via FS.

DCTPP1, an Oncogene Regulated by miR-378a-3p, Promotes Proliferation of Breast Cancer via DNA Repair Signaling Pathway

Breast cancer (BRCA) is one of the most deadly cancers worldwide, with poor survival rates that could be due to its high proliferation. Human all-alpha dCTP pyrophosphatase 1 (DCTPP1) is implicated in numerous diseases, including cancers. However, its role in BRCA is unclear. In this study, we used bioinformatic analyses of the ONCOMINE, UALCAN, and GEPIA databases to determine the expression pattern of DCTPP1 in BRCA. We found that elevated DCTPP1 levels correlate with poor BRCA prognosis. DCTPP1 silencing inhibited BRCA cell proliferation and induced apoptosis in vitro, as well as in vivo. Our data show that this tumorigenic effect depends on DNA repair signaling. Moreover, we found that DCTPP1 is directly modulated by miR-378a-3p, whose downregulation is linked to BRCA progression. Our results showed down-regulation of miR-378a-3p in BRCA. Upregulation of miR-378a-3p, on the other hand, can inhibit BRCA cell growth and proliferation. This study shows that reduced miR-378a-3p level enhances DCTPP1 expression in BRCA, which promotes proliferation by activating DNA repair signaling in BRCA.

A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

Simple Summary

Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients.

Abstract

In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.

Overall Survival Benefit in Rectal Cancer After Neoadjuvant Radiotherapy and Adjuvant Chemotherapy: A Propensity-Matched Population-Based Study

Background

It is well known that neoadjuvant radiotherapy could reduce local recurrence followed by surgical resection. However, evidence about oncologic efficacy of radiotherapy and survival benefit of adjuvant chemotherapy after neoadjuvant radiotherapy is still lacking.

Methods

This retrospective propensity score-matched cohort study identified patients with pathologically confirmed rectal cancer and receiving surgery with curative intent from the Surveillance, Epidemiology, and End Results database from 2004 through 2014. Overall survival was compared using the stratified log-rank test. Multivariate Cox regression analysis was used for identifying risk factor and developing prediction nomogram.

Results

A total of 22,008 (11,004 for each group) propensity-matched patients were identified. In the context of receiving adjuvant chemotherapy after surgical resection, there was no significant difference in terms of overall survival between surgery alone group and neoadjuvant radiotherapy and surgery group, whether for stage I (log-rank test p = 0.467), stage II (log-rank test p = 0.310), or stage III (p = 0.994). In case of receiving a prior combination therapy of neoadjuvant radiotherapy and surgery, the following adjuvant chemotherapy could significantly improve overall survival for patients with stage I (log-rank test p <0.001), stage II (log-rank test p = 0.038), and stage III (log-rank test p = 0.014). Nomogram integrating clinicopathologic factors was developed to predict survival benefit associated with neoadjuvant radiotherapy. Calibration and ROC curves validated promising performance for the nomogram.

Conclusion

Patients with rectal cancer underwent neoadjuvant radiotherapy yield acceptable outcomes and are more likely to benefit from adjuvant chemotherapy in terms of overall survival. These data would be evidential for advocating consistency in guideline adherence to the use of adjuvant chemotherapy after neoadjuvant radiotherapy.